Method for automated detection of defects in cast wheel products

ABSTRACT

Provided in the present invention is a method for automated detection of defects in cast wheel products, comprising three stages of sample preprocessing, offline training, and online inspection. Specific steps comprise collection of training samples; sample preprocessing; categorization of samples in three major types as spokes, rims and hubs, then processing; by means of the three sample types, spokes, rims and hubs, training a network offline, then generating online detectors for said three sample types respectively; importing the trained CNN spoke defect detector, CNN rim defect detector and CNN hub defect detector to a host computer, and locating same on an automated production inspection line; wholly automated online defect detection; output of identified detected defects by an output mode according to user requirements. The automated inspection method of the present invention is particularly robust in respect of the illumination, angle of photography and positioning that prevail during the inspection process, and, with a high degree of automation, provides accurate defect inspection with no need for the participation of an operator adjusting minor parameters.

FIELD OF INVENTION

The present invention relates to the field of computer vision. Moreparticularly, it relates to an automatic detecting technique to revealcasting defects on wheels.

BACKGROUND

Various defects, on wheel products that are formed in the castingprocess, occur internally due to the technology, the design, thematerials and the facilities etc. The American Society for Testing andMaterials (ASTM) has set the standards and reference pictures fordefects in casting products, and set the levels of influences on productqualities by defects in the light of the size of the defects outercontours and the ratio of the area of the defects to a unit area.Usually, during inspection, the casting products are imaged by X-rayequipment, the revealed defects are compared with reference defects instandard pictures. If the defect areas surpass the quality standardchosen by the user, that is the defect areas surpasses a certain size,the wheel quality is judged as unacceptable.

Defect inspection of casting products includes manual as well as machineinspection.

Manual inspection is where the operator judges by eye whether a castingdefect that appears on the screen of the X ray machine is within thequality level of acceptable products depending on the size of the defectand its shape etc. The common problems of manual inspection arepersonnel fatigue due to the long hours required for the inspection andidentification work, the inefficiency of the process of defectrecognition, the instability of judging standards, the subjectivity ofthe results, etc.

At present, most manufacturing lines rely on manual inspection, mainlybecause the automatic image recognition technologies of machines cannotmeet the required quality control standard required of the manufacturinglines. Their precision in judging whether the defects exist or not ispoor, and so they are unable to accurately determine the quality levelsof the products.

Automatic machine inspection for defects has been the subject oftechnological development for a long time. The prior arts are mainlyreflected in two aspects of the technical methods. The first one is theimage processing methods used to evaluate low-level features of theimages. The second one is the multi-perspective image fusion analysismethod coming from stereoscopic vision.

With image analysis and processing techniques like de-noising,enhancement, regional division, edge extraction, contour closing, objectsegmentation, object padding, etc. which are focused on the low-levelfeatures of the images; these methods are the first steps in determiningwhether there are any defects in the product image. The grade of thedefect is first specified and then it is finally determined whether theproducts meet the required standard by calculating the area of thetarget defect, measuring its contours' perimeter as well as thepercentage of the defective area per unit product area, and so on.

This method, however, completely relies on the low-level features ofimages and involves numerous parameter adjustments, which not onlyrequire a proficient professional operator, but more importantly thefrequent adjustment of parameters means that the inspection criteria arecontinually changing which decreases the precision of the defectinspection that can be achieved.

The primary reason for the errors in this kind of technology is that theshape and grayscales of the casting defects are random variables and thelow-level image features fail to express and depict invisible features,or are specified as high-level features. The other kind of method:multi-perspective image fusion analysis, has also been under developmentfor a long time. By employing space imaging and judging the existence ofdefects from many perspectives, the size and the grade of defects can beascertained more precisely. Theoretically speaking, this kind of methodis still based on low-level image features. It is not essentiallydifferent from previous methods merely recognizing target defects onimage shots from different perspectives, using images fusion techniquesto increase the accuracy of recognition. Most importantly it also relieson a large number of parameter settings and adjustments. Moreover, thismethod increases costs due to its additional hardware. Besides, thesemethods are not very reliable because the technologies for detectingcasting defects for a single image are still under development.

SUMMARY OF THE INVENTION

The aim of the invention is to overcome the shortcomings and theinsufficiency of existing technologies, and provide an automatic methodof defect detection for wheel shaped casting products.

The purpose of the invention is realized by the following technicalscheme:

An automatic defect detection method using neural networks for wheelshaped casting products which includes three stages: samplepre-processing, off-line training and online testing, comprise of thefollowing steps:

(S1) collecting over 60,000 training samples and ensuring the samplenumbers of spokes, rims and axles are greater than 20,000 respectively.The ratio of said positive and negative samples is set at 1:2;

(S2) pre-processing the aforementioned samples;

(S3) classifying the said samples into three classes of spoke, rim andaxle for subsequent processing;

(S4) offline training using the samples of spoke, rim and axlerespectively, and then obtaining online useable defect detectors ofspoke, rim and axle;

(S5) loading the well-trained defect detectors based on convolutionalneural networks for spokes, rims and axles into the upper computer andplaced on to the automatic inspection production line;

(S6) inspect defects online automatically;

(S7) according to the user's requirements, a defect image or alarmsignal is output by the defect inspection system.

The phase of pre-processing samples in the said step (S2) furthercomprise:

(S2-1) smoothing and filtering the collected wheel hub images byutilizing a 3×3 domain template in order to eliminate noise whichemerges from the imaging process;

(S2-2) using a gradient sharpening algorithm to process the images. Allpixels are processed one by one. Adding the absolute difference betweenthe current pixel and the next pixel in the same line to the absolutedifference between the current pixel and the next pixel in the nextline. Comparing the sums with a set threshold, if the sum is greaterthan the threshold, take this result as the current pixel. Doing so canemphasize profiles and facilitate analysis;

(S2-3) equalizing the histogram of the image obtained from the step(S2-2);

(S2-4) normalizing the image obtained from the step (S2-3) in order toaccelerate the astringency of the network training.

The said step (S3) divides samples into three main categories of spoke,rim and axle, which further comprise of the specific processing steps:

(S3-1) dividing an image sample into small images of M×M, where M cantake the values of 80, 100 or 120. Marking every one of the small imagesas negatives or positives depending on whether a defect exists or not.

(S3-2) training convolutional neural networks with samples got from step(S3-1). In order to enhance the robustness of the detectors, all imagesamples are slightly resized (0.96 to 1.08 times), contrast stretched(contrast coefficient from 0.8 to 1.2) and rotated (−60° to 60°, eachtime each time up to 5 degrees) randomly.

(S3-3) when small-lot samples are called, the number of samples can be64. The samples are flipped horizontally and Gaussian noise are addedrandomly. N×N pixel areas are chosen from the transformed small-lotsamples as the training samples for the convolutional neural networks.For instance, N can be 96 if M is 100 or 120 to increase samplediversity, which presents an effective approach to gain bettergeneralization ability.

The step (S4) of training defect detectors, which is generating neuralnetworks models to inspect the defects for wheel hubs, further comprise:

(S4-1) making use of the samples of spoke, rim and axle images;employing a Back Propagation (BP) algorithm to train the neural networkfor defects in the spokes, rims and axles respectively. Using smallestlot samples to calculate the error and update the connection weights inevery loop;

(S4-2) setting the learning rate as 0.01;

(S4-3) in every loop, setting 64 as the number of small-lot and updatingparameters with average errors;

(S4-4) designing neural networks. The defect detecting neural networksfor wheel shaped products are multilayer convolutional neural networks,which automatically learn features supervised from a large number ofsamples. The input is an image, and the output is the classified labelof the image. The neuron number is arranged as the pixel number of theinput image, while only one neuron node is arranged in the output layerand the output is the classification result of the image. The defectdetecting neural networks consists of two parts: the first part is amultistage feature extractor that alternately combines convolutionallayers, down-sampling layers and local response normalization layers,and executes convolution, down-sampling and nonlinear transformation;the second part is a classifier that is a fully connected neural networkconsisting of two fully connected layers, which is trained with a backpropagation algorithm and can classify the image features of wheel hubsextracted from the said first part correctly. In this technical scheme,the feature extraction of the defect detecting neural networks containstwo stages. The first stage is an extraction of low-level features, suchas dots and lines. The second is a linear combination of low-levelfeatures to generate high-level features via back propagation training.

(S4-5) when classifying samples, the defect detecting neural networkdivides an image of the wheel shaped product into small images of M×M.To every small image, when M is 100, it takes five 96×96 subareas fromthe upper left corner, the upper right corner, the lower left corner,the lower right corner and the center of the image as inputs of theconvolution neural network, calculating the mean value of the fiveoutputs of the said neural networks, and judging whether this area isdefective or not according to the final mean value. Setting [0, 1] asthe output range, if the output mean value is greater than the setthreshold 0.5, it indicates this area in the image of wheel shapedproduct as defective, otherwise not.

(S4-6) stop training the neural networks when they meet the set accuracyon the test image set, and become effective defect detectors.

When detecting online, the detecting process follows the detectionprocedure from axle to spoke then to rim, automatically obtaining onlineimages from different working positions in a sequential manner.

The online automatic inspection stage of the defect detecting process ofstep (S6) further comprises:

(S6-1) obtaining the axle image of a wheel hub, pre-processing it byutilizing the same offline method of pre-processing image samples, thenuse the axle detector to inspect for defects in each axle image. If anydefects are found, their regions are marked and stored the image, whichis named as its precise date and time of inspection;

(S6-2) obtaining the spoke image of a wheel hub, pre-processing it byutilizing the same offline method of pre-processing image samples, thenuse the spoke detector to inspect for defects in each spoke image. Ifany defects are found, their regions are marked and stored the image,which is named as its precise date and time of inspection;

(S6-3) obtaining the rim image of a wheel hub, pre-processing it byutilizing the same offline method of pre-processing image samples, thenuse the rim detector to inspect for defects in each rim image. If anydefects are found, their regions are marked and stored the image, whichis named as its precise date and time of inspection;

(S6-4) according to the user's requirements, an image or alarm signal isoutput by the defect inspection system.

(S6-5) at steps S6-1 to S6-3, if defects are found in any images, sendthe wheel to the unqualified product zone, otherwise to the qualifiedone.

Compared to prior arts, the present invention has the followingadvantages and beneficial effects:

-   -   1. In the training phase, the convolutional neural networks        learns essential features from positive and negative samples of        spokes, rims and axles, those features are more identifiable and        classable than manually extracted ones;    -   2. Because the convolutional neural network has a certain        robustness for identifying some degrees of displacement,        scaling, and other forms of distortion in the images, the        trained detectors for wheel defects have a degree of robustness        in practical wheel hub defect inspection, and have a better        performance for wheel hub defects that are not similar in shape;    -   3. The detector has a strong robustness for dealing with        variations in illumination and pose;    -   4. Because the inspection procedures require no human        intervention, the wheel hub inspection can introduce a high        level of automation, high production efficiency, simple        operation and low operation cost.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram of the algorithm module illustrating the defectinspection approach for wheel shaped products;

FIG. 2 is a flow chart illustrating the said automatic defect detectionmethod for wheel shaped products;

FIG. 3 is a schematic of the internal relations and structures of neuralnetworks for wheel hub defect detection used for the said method in FIG.2;

FIG. 4 is an original spoke sample image of a real product;

FIG. 5 is the grey scale histogram of FIG. 4;

FIG. 6 is an image showing the preprocessing result of FIG. 4;

FIG. 7 is an image showing the maximum and secondary maximum of thehistogram of FIG. 6;

FIGS. 8-1 to 8-3 are images showing the defect inspection results of aspoke, rim and axle respectively.

DETAILED DESCRIPTION

The present method will be described in greater detail by referring tothe following embodiment and drawings that are attached to the presentmethod. However, the said method can be employed in terms of otherembodiments.

FIG. 1 illustrates the two working parts of the present method: theoffline and the online. The offline part offers the work basis for theonline one, the online part carries out an online inspection and therecognition process for defects according to the method and detectorsgenerated by the offline part.

The neural network based automatic defect detection method and systemfor wheel shaped products comprises of the following steps:

(S1) collecting over 60,000 training samples and ensuring the samplenumbers of spokes, rims and axles are greater than 20,000 respectively.The ratio of the said positive and negative samples is set 1:2;

(S2) pre-processing the aforementioned samples;

(S3) classifying the said samples into three classes of spoke, rim andaxle for subsequent process;

(S4) offline training using the samples of spoke, rim and axlerespectively, and then obtaining online useable defect detectors ofspoke, rim and axle;

(S5) loading the well-trained convolutional neural network defectdetectors for spokes, rims and axles into the upper computer which isplaced on to the automatic inspection production line;

(S6) inspect defects online automatically;

(S7) according to the user's requirements, the image or alarm signal isoutput by the defect inspection system.

The specific procedures of the wheel shaped product defect detectingmethod that is based on the convolutional neural network is illustratedas FIG. 2, which involves three major phases: pre-processing samples,offline training and online inspection. The said pre-processing phase ofthe samples includes the following steps:

(S2-1) smoothing and filtering the collected wheel hub images byutilizing a 3×3 domain template in order to eliminate noise whichemerges from the imaging process;

(S2-2) using a gradient sharpening algorithm to process the images. Allpixels are processed one by one. Adding the absolute difference betweenthe current pixel and the next pixel in the same line to the absolutedifference between the current pixel and the next pixel in the nextline. Comparing the sums with a set threshold, if the sum is greaterthan the threshold, take this result as the current pixel. Doing so canemphasize profiles and facilitate analysis;

(S2-3) equalizing the histogram of the image obtained from the step(S2-2);

(S2-4) normalizing the image obtained from the step (S2-3) in order toaccelerate the astringency of the network training.

At the said offline training phase, firstly, classify samples into threeclasses of spoke, rim and axle and process them. The specific proceduresfor processing include:

(S3-1) cutting an image sample apart into the small images of M×M,wherein M can take the values 80, 100 or 120. Marking every small imageas a negative or positive sample in light of whether a defect exists ornot.

(S3-2) training the convolutional neural network with samples got fromstep (S3-1). In order to enhance the robustness of the detectors, allimage samples are slightly resized (0.96 to 1.08 times), contraststretched (with a contrast coefficient of 0.8 to 1.2) and rotated (−60°to 60°, each time up to 5 degrees) randomly;

(S3-3) when small-lot samples are called, the number of samples can be64. The samples are flipped horizontally and Gaussian noise are addedrandomly. N×N pixel areas are chosen from the transformed small-lotsamples as the training samples for the convolutional neural networks.For instance, N can be 96 if M is 100 or 120 to increase samplediversity, which presents an effective approach to gain bettergeneralization ability.

The training of defect detecting neural networks (i.e. defect detectors)contains:

(S4-1) making use of the samples of spoke, rim and axle images;employing a Back Propagation (BP) algorithm to train the neural networkfor defects in the spokes, rims and axles respectively. Using smallestlot samples to calculate the error and update the connection weights inevery loop;

(S4-2) setting the learning rate as 0.01;

(S4-3) in every loop, setting 64 as the number of the small-lot andupdating parameters with the average error;

(S4-4) designing neural networks. The defect detecting neural networksfor wheel shaped products are multilayer convolutional neural networks,which automatically learn features supervised from a large number ofsamples. The input is an image, and the output is the classified labelof the image. The neuron number is arranged as the pixel number of theinput image, while only one neuron node is arranged in the output layerand the output is the classification result of the image. The defectdetecting neural networks consists of two parts: the first part is amultistage feature extractor that alternately combines convolutionallayers, down-sampling layers and local response normalization layers,and executes convolution, down-sampling and nonlinear transformation;the second part is a classifier that is a fully connected neural networkconsisting of two fully connected layers, which is trained with a backpropagation algorithm and can classify the image features of wheel hubsextracted from the said first part correctly. In this technical scheme,the feature extraction of the defect detecting neural networks containstwo stages. The first stage is an extraction of low-level features, suchas dots and lines. The second is a linear combination of low-levelfeatures to generate high-level features via back propagation training.

(S4-5) when classifying samples, the defect detecting neural networkdivides an image of the wheel shaped product into small images of MxM.To every small image, when M is 100, it takes five 96×96 subareas fromthe upper left corner, the upper right corner, the lower left corner,the lower right corner and the center of the image as inputs of theconvolution neural network, calculating the mean value of the fiveoutputs of the said neural networks, and judging whether this area isdefective or not according to the final mean value. Setting [0, 1] asthe output range, if the output mean value is greater than the setthreshold 0.5, it indicates this area in the image of wheel shapedproduct as defective, otherwise not.

(S4-6) stop training the neural networks when they meet the set accuracyon the test image set, and become effective defect detectors.

The said online inspection stage comprises the following procedure:

When detecting online, the detecting process follows the detectionprocedure from axle to spoke then to rim, automatically obtaining onlineimages from different working positions in a sequential manner, thesteps are as follows:

(S6-1) obtaining the axle image of a wheel hub, pre-processing it byutilizing the same offline method of pre-processing image samples, thenuse the axle detector to inspect for defects in each axle image. If anydefects are found, their regions are marked and stored the image, whichis named according to its inspection time and date;

(S6-2) obtaining the spoke image of a wheel hub, pre-processing it byutilizing the same offline method of pre-processing image samples, thenuse the spoke detector to inspect for defects in each spoke image. Ifany defects are found, their regions are marked and stored the image,which is named according to its inspection time and date;

(S6-3) obtaining the rim image of a wheel hub, pre-processing it byutilizing the same offline method of pre-processing image samples, thenuse the rim detector to inspect for defects in each rim image. If anydefects are found, their regions are marked and stored the image, whichis named according to its inspection time and date;

(S6-4) according to the user's requirements, an image or alarm signal isoutput by defect inspection system.

(S6-5) at the said steps S6-1 to S6-3; if defects are found in anyimages, the wheel is sent to the unqualified product zone; otherwise tothe qualified zone.

FIG. 3 is a schematic of the internal relations and structures of neuralnetworks for wheel hub defect detection, wherein the output function ofneurons for all convolution layers is RELU function, the pooling of allpools is the maximum pooling and the final output layer uses the Softmaxlayer. The present method collects actual images of wheel hubs from amanufacturing line and inspects the collected images by the said defectdetecting approach.

FIG. 4 is a spoke image with defects. As shown in FIG. 4, in theoriginal image the contrast between the defects and their background isvery low.

FIG. 5 is the grey scale histogram of the image in FIG. 4. As shown inFIG. 5, the image contains redundant information.

FIG. 6 is an image showing the preprocessing result of FIG. 4. As shownin FIG. 6, the contrast between the defects and their background in theimage is higher than before, which makes the defects more obvious.

FIG. 7 is an image showing maximum and second peak positions in the greyscale histogram of the image in FIG. 6, which matches the range of thegrayscale of the original image.

FIGS. 8-1-8-3 are result images showing the defect inspection of spoke,rim and axle respectively in one set of test samples. As shown in FIGS.8-1-8-3, well-trained defect detectors can inspect and recognize thedefects correctly and locate their positions on spokes, rims and axles.From the results of aforementioned set of test samples, the said methodshows an excellent recognition capability in a practical industrialapplication.

While certain features of the invention have been illustrated anddescribed herein, many modifications, substitutions, changes, andequivalents will now occur to those of ordinary skill in the art. It is,therefore, to be understood that the appended claims are intended tocover all such modifications and changes as fall within the true spiritof the invention.

1. A method for the automatic detection of defects in wheel hub shapedcasting products. The said method is characterized in three parts:preprocessing samples; offline training; online inspection. The detailedprocedure of the said method comprising: (S1) the collection of trainingsamples; (S2) preprocessing of collected samples; (S3) classifying thesaid samples into three classes of spoke, rim and axle for subsequentprocess; (S4) offline training using the samples of spoke, rim and axlerespectively, and then obtaining online useable defect detectors ofspoke, rim and axle; (S5) loading the well-trained convolutional neuralnetwork defect detectors for spokes, rims and axles into the uppercomputer which is placed on to the automatic inspection production line;(S6) inspect defects online automatically; (S7) according to the user'srequirements, the image or alarm signal is output by the defectinspection system.
 2. The method of claim 1, wherein said procedure (S6)is characterized in these sub steps: (S6-1) obtaining the axle image ofa wheel hub, pre-processing it by utilizing the same offline method ofpre-processing image samples, then use the axle detector to inspect fordefects in each axle image. If any defects are found, their regions aremarked and stored the image, which is named as its precise date and timeof inspection; (S6-2) obtaining the spoke image of a wheel hub,pre-processing it by utilizing the same offline method of pre-processingimage samples, then use the spoke detector to inspect for defects ineach spoke image. If any defects are found, their regions are marked andstored the image, which is named as its precise date and time ofinspection; (S6-3) obtaining the rim image of a wheel hub,pre-processing it by utilizing the same offline method of pre-processingimage samples, then use the rim detector to inspect for defects in eachrim image. If any defects are found, their regions are marked and storedthe image, which is named as its precise date and time of inspection;(S6-4) according to the user's requirements, an image or alarm signal isoutput by defect inspection system. (S6-5) at the said steps S6-1 toS6-3; if defects are found in any images, the wheel is sent to theunqualified product zone; otherwise to the qualified zone.
 3. The methodof claim 1, wherein said procedure (S2) is characterized in these substeps: (S2-1) smoothing and filtering the collected wheel hub images byutilizing a 3×3 domain template in order to eliminate noise whichemerges from the imaging process; (S2-2) using a gradient sharpeningalgorithm to process the images. All pixels are processed one by one.Adding the absolute difference between the current pixel and the nextpixel in the same line to the absolute difference between the currentpixel and the next pixel in the next line. Comparing the sums with a setthreshold, if the sum is greater than the threshold, take this result asthe current pixel. Doing so can emphasize profiles and facilitateanalysis; (S2-3) equalizing the histogram of the image obtained from thestep (S2-2); (S2-4) normalizing the image obtained from the step (S2-3).4. The method of claim 1, wherein said procedure (S3) is characterizedin these sub steps: (S3-1) dividing the image samples into MxM smallerimages, wherein ones which contain defects are marked as negativesamples, others are marked as positive samples; (S3-2) training aconvolutional neural network with samples from sub step (S3-1). In orderto enhance the detectors' robustness, all image samples are slightlyresized, rotated and contrast stretched randomly; (S3-3) when small-lotsamples are called, the number of samples can be
 64. The samples areflipped horizontally and Gaussian noise are added randomly. N×N pixelareas are chosen from the transformed small-lot samples as the trainingsamples for the convolutional neural networks. For instance, N can be 96if M is 100 or 120 to increase sample diversity, which presents aneffective approach to gain better generalization ability.
 5. The methodof claim 4, wherein in sub step (S3-1) said number M is chosen from 80,100 or 120;
 6. The method of claim 4, wherein in sub step (S3-1) theratio of the said positive and negative samples is set at 1:2.
 7. Themethod of claim 1, wherein said procedure (S4) is characterized in thesesub steps: (S4-1) making use of the samples of spoke, rim and axleimages; employing a Back Propagation (BP) algorithm to train the neuralnetwork for defects in the spokes, rims and axles respectively. Usingsmallest lot samples to calculate the error and update the connectionweights in every loop; (S4-2) setting the learning rate as 0.01; (S4-3)setting 64 as the amount of the small-lot; (S4-4) neural networksincluding low-level feature extraction as the first phase and BPalgorithm training as the second phase, which linearly combineslow-level features and forms high-level features; (S4-5) inspectingdefective areas of wheel products; (S4-6) stop training the neuralnetworks when they meet the set accuracy on the test image set, andbecome effective defect detectors.
 8. The method of claim 1, whereinsaid procedure (S4-5) is characterized in these sub steps: whenclassifying samples, the defect detecting neural network divides animage of the wheel shaped product into MxM small sample images. To everysmall sample image, when M is 100, it takes five 96×96 subareas from theupper left corner, the upper right corner, the lower left corner, thelower right corner and the center of the image as inputs of theconvolution neural network, calculating the mean value of the fiveoutputs of the said neural networks, and judging whether this area isdefective or not according to the final mean value. Setting [0, 1] asthe output range, if the output mean value is greater than the setthreshold 0.5, it shows this area in the image of wheel shaped productas defective, otherwise not.
 9. The method of claim 1 is characterizedin that the said sample size is greater than 60,000; the sample sizes ofspokes, rims and axles are all greater than 20,000.
 10. The method ofclaim 4, wherein said procedure (S4-5) is characterized in these substeps: when classifying samples, the defect detecting neural networkdivides an image of the wheel shaped product into MxM small sampleimages. To every small sample image, when M is 100, it takes five 96×96subareas from the upper left corner, the upper right corner, the lowerleft corner, the lower right corner and the center of the image asinputs of the convolution neural network, calculating the mean value ofthe five outputs of the said neural networks, and judging whether thisarea is defective or not according to the final mean value. Setting [0,1] as the output range, if the output mean value is greater than the setthreshold 0.5, it shows this area in the image of wheel shaped productas defective, otherwise not.